The quest for high-performance construction materials is led by the development and application of new reinforcement materials for cement composites. Concrete reinforcement with fibers has a long history. Nowadays, many new fibers associated with high performance and possessing eco-environmental characteristics, such as basalt fibers and plant fibers, have received much attention from researchers. In addition, nanomaterials are considered as a core material in the modification of cement composites, specifically in the enhancement of the strength and durability of composites. This paper provides an overview of the recent research progress on cement composites reinforced with fibers and nanomaterials. The influences of fibers and nanomaterials on the fresh and hardened properties of cement composites are summarized. Moreover, future trends in the application of these fibers or of nanomaterial-reinforced cement composites are proposed.
Today, the most commonly used civil infrastructure inspection method is based on a visual assessment conducted by certified inspectors following prescribed protocols. However, the increase in aggressive environmental and load conditions, coupled with the achievement of many structures of the life-cycle end, has highlighted the need to automate damage identification and satisfy the number of structures that need to be inspected. To overcome this challenge, this paper presents a method for automating concrete damage classification using a deep convolutional neural network. The convolutional neural network was designed after an experimental investigation of a wide number of pretrained networks, applying the transfer-learning technique. Training and validation were conducted using a database built with 1352 images balanced between “undamaged”, “cracked”, and “delaminated” concrete surfaces. To increase the network robustness compared to images in real-world situations, different image configurations have been collected from the Internet and on-field bridge inspections. The GoogLeNet model, with the highest validation accuracy of approximately 94%, was selected as the most suitable network for concrete damage classification. The results confirm that the proposed model can correctly classify images from real concrete surfaces of bridges, tunnels, and pavement, resulting in an effective alternative to the current visual inspection techniques.
Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro- or fine-scale. Among existing methods for homogenization, computational homogenization is widely used in multiscale analyses of structures and materials. Conventional computational homogenization suffers from long computing times, which substantially limits its application in analyzing engineering problems. The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response. Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set. This article intends to model the multiscale constitution using feedforward neural network (FNN) and recurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths. Applications to two-dimensional multiscale analysis are tested and discussed in detail.
This article proposes a novel methodology that uses mathematical and numerical models of a structure to build a data set and determine crucial nodes that possess the highest sensitivity. Regression surfaces between the structural parameters and structural output features, represented by the natural frequencies of the structure and local transmissibility, are built using the numerical data set. A description of a possible experimental application is provided, where sensors are mounted at crucial nodes, and the natural frequencies and local transmissibility at each natural frequency are determined from the power spectral density and the power spectral density ratios of the sensor responses, respectively. An inverse iterative process is then applied to identify the structural parameters by matching the experimental features with the available parameters in the myriad numerical data set. Three examples are presented to demonstrate the feasibility and efficacy of the proposed methodology. The results reveal that the method was able to accurately identify the boundary coefficients and physical parameters of the Euler-Bernoulli beam as well as a highway bridge model with elastic foundations using only two measurement points. It is expected that the proposed method will have practical applications in the identification and analysis of restored structural systems with unknown parameters and boundary coefficients.
The physio-chemical changes in concrete mixes due to different coarse aggregate (natural coarse aggregate and recycled coarse aggregate (RCA)) and mix design methods (conventional method and Particle Packing Method (PPM)) are studied using thermogravimetric analysis of the hydrated cement paste. A method is proposed to estimate the degree of hydration (
Some of the current concrete damage plasticity models in the literature employ a single damage variable for both the tension and compression regimes, while a few more advanced models employ two damage variables. Models with a single variable have an inherent difficulty in accounting for the damage accrued due to tensile and compressive actions in appropriately different manners, and their mutual dependencies. In the current models that adopt two damage variables, the independence of these damage variables during cyclic loading results in the failure to capture the effects of tensile damage on the compressive behavior of concrete and vice-versa. This study presents a cyclic model established by extending an existing monotonic constitutive model. The model describes the cyclic behavior of concrete under multiaxial loading conditions and considers the influence of tensile/compressive damage on the compressive/tensile response. The proposed model, dubbed the enhanced concrete damage plasticity model (ECDPM), is an extension of an existing model that combines the theories of classical plasticity and continuum damage mechanics. Unlike most prior studies on models in the same category, the performance of the proposed ECDPM is evaluated using experimental data on concrete specimens at the material level obtained under cyclic multiaxial loading conditions including uniaxial tension and confined compression. The performance of the model is observed to be satisfactory. Furthermore, the superiority of ECDPM over three previously proposed constitutive models is demonstrated through comparisons with the results of a uniaxial tension-compression test and a virtual test.
The tensile behavior of hybrid fiber reinforced concrete (HFRC) is important to the design of HFRC and HFRC structure. This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC. This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23 features of HFRC. In the model, three methods to process output features (no-processed, mid-processed, and processed) are discussed and the mid-processed method is recommended to achieve a better reproduction of the experimental data. This means the strain should be normalized while the stress doesn’t need normalization. To prepare the database of the model, both many direct tensile test results and the relevant literature data are collected. Moreover, a traditional equation-based model is also established and compared with the ANN model. The results show that the ANN model has a better prediction than the equation-based model in terms of the tensile stress-strain curve, tensile strength, and strain corresponding to tensile strength of HFRC. Finally, the sensitivity analysis of the ANN model is also performed to analyze the contribution of each input feature to the tensile strength and strain corresponding to tensile strength. The mechanical properties of plain concrete make the main contribution to the tensile strength and strain corresponding to tensile strength, while steel fibers tend to make more contributions to these two items than PVA fibers.
In this paper, we report the turbulent flow structures and the scour geometry around two piers with different diameters. An experiment was conducted on a non-uniform sand bed with two types of tandem arrangements, namely, pier (T1) with a 75 mm front and 90 mm rear, and pier (T2) with a 90 mm front and 75 mm rear, with and without-seepage flows, respectively. A strong wake region was observed behind the piers, but the vortex strength diminished with downward seepage. Streamwise velocity was found to be maximum near the bed downstream of the piers and at the edge of the scour hole upstream of the piers. Quadrant analysis was used to recognize the susceptible region for sediment entrainment and deposition. Upstream of the piers near the bed, the moments, turbulent kinetic energy (TKE), and TKE fluxes were found to decrease with downward seepage, in contrast to those in a plane mobile bed without piers. The reduction percentages of scour depth at the rear pier compared with the front one were approximately 40% for T1 and 60% for T2. Downward seepage also resulted in restrained growth of scouring with time.
This paper presents the results of fire resistance tests on carbon fiber-reinforced polymer (CFRP) strengthened concrete flexural members, i.e., T-beams and slabs. The strengthened members were protected with fire insulation and tested under the combined effects of thermal and structural loading. The variables considered in the tests include the applied load level, extent of strengthening, and thickness of the fire insulation applied to the beams and slabs. Furthermore, a previously developed numerical model was validated against the data generated from the fire tests; subsequently, it was utilized to undertake a case study. Results from fire tests and numerical studies indicate that owing to the protection provided by the fire insulation, the insulated CFRP-strengthened beams and slabs can withstand four and three hours of standard fire exposure, respectively, under service load conditions. The insulation layer impedes the temperature rise in the member; therefore, the CFRP–concrete composite action remains active for a longer duration and the steel reinforcement temperature remains below 400°C, which in turn enhances the capacity of the beams and slabs.
Natural slopes consist of non-homogeneous soil profiles with distinct characteristics from slopes made of homogeneous soil. In this study, the limit equilibrium modified pseudo-dynamic method is used to analyze the stability of two-layered c-φ soil slopes in which the failure surface is assumed to be a logarithmic spiral. The zero-stress boundary condition at the ground surface under the seismic loading condition is satisfied. New formulations derived from an analytical method are proposed for the predicting the seismic response in two-layered soil. A detailed parametric study was performed in which various parameters (seismic accelerations, damping, cohesion, and angle of internal friction) were varied. The results of the present method were compared with those in the available literature. The present analytical analysis was also verified against the finite element analysis results.
A theoretical solution is aimed to be developed in this research for predicting the failure in internally pressurized composite pressure vessels exposed to low-velocity impact. Both in-plane and out-of-plane failure modes are taken into account simultaneously and thus all components of the stress and strain fields are derived. For this purpose, layer-wise theory is employed in a composite cylinder under internal pressure and low-velocity impact. Obtained stress/strain components are fed into appropriate failure criteria for investigating the occurrence of failure. In case of experiencing any in-plane failure mode, the evolution of damage is modeled using progressive damage modeling in the context of continuum damage mechanics. Namely, mechanical properties of failed ply are degraded and stress analysis is performed on the updated status of the model. In the event of delamination occurrence, the solution is terminated. The obtained results are validated with available experimental observations in open literature. It is observed that the sequence of in-plane failure and delamination varies by increasing the impact energy.
The aim of this study is to propose a new detection method for determining the damage locations in pile foundations based on deep learning using acoustic emission data. First, the damage location is simulated using a back propagation neural network deep learning model with an acoustic emission data set acquired from pile hit experiments. In particular, the damage location is identified using two parameters: the pile location (PL) and the distance from the pile cap (DS). This study investigates the influences of various acoustic emission parameters, numbers of sensors, sensor installation locations, and the time difference on the prediction accuracy of PL and DS. In addition, correlations between the damage location and acoustic emission parameters are investigated. Second, the damage step condition is determined using a classification model with an acoustic emission data set acquired from uniaxial compressive strength experiments. Finally, a new damage detection and evaluation method for pile foundations is proposed. This new method is capable of continuously detecting and evaluating the damage of pile foundations in service.
The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability (LLDV) when determining whether liquefaction is likely to cause damage at the ground’s surface. This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network (BBN) methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model. The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learning (ML) algorithm-K2 and domain knowledge (DK) data fusion methodology. Compared with the C4.5 decision tree-J48 model, naive Bayesian (NB) classifier, and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen’s kappa coefficient, the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage. The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations, and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development. The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling. This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefied sites based on an engineering point of view.
In this study, the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were devised. Silica fume was used at concentrations of 0%, 5%, 10%, and 20%. Cube specimens (100 mm × 100 mm × 100 mm) were prepared for testing the compressive strength and ultrasonic pulse velocity. They were cured at 20°C±2°C in a standard cure for 7, 28, and 90 d. After curing, they were subjected to temperatures of 20°C, 200°C, 400°C, 600°C, and 800°C. Two well-known deep learning approaches, i.e., stacked autoencoders and long short-term memory (LSTM) networks, were used for forecasting the compressive strength and ultrasonic pulse velocity of concrete containing silica fume subjected to high temperatures. The forecasting experiments were carried out using MATLAB deep learning and neural network tools, respectively. Various statistical measures were used to validate the prediction performances of both the approaches. This study found that the LSTM network achieved better results than the stacked autoencoders. In addition, this study found that deep learning, which has a very good prediction ability with little experimental data, was a convenient method for civil engineering.
Permeability is a major indicator of concrete durability, and depends primarily on the microstructure characteristics of concrete, including its porosity and pore size distribution. In this study, a variety of concrete samples were prepared to investigate their microstructure characteristics via nuclear magnetic resonance (NMR), mercury intrusion porosimetry (MIP), and X-ray computed tomography (X-CT). Furthermore, the chloride diffusion coefficient of concrete was measured to explore its correlation with the microstructure of the concrete samples. Results show that the proportion of pores with diameters<1000 nm obtained by NMR exceeds that obtained by MIP, although the difference in the total porosity determined by both methods is minimal. X-CT measurements obtained a relatively small porosity; however, this likely reflects the distribution of large pores more accurately. A strong correlation is observed between the chloride diffusion coefficient and the porosity or contributive porosity of pores with sizes<1000 nm. Moreover, microstructure parameters measured via NMR reveal a lower correlation coefficient R2 versus the chloride diffusion coefficient relative to the parameters determined via MIP, as NMR can measure non-connected as well as connected pores. In addition, when analyzing pores with sizes>50 µm, X-CT obtains the maximal contributive porosity, followed by MIP and NMR.
The utilization of urban underground space (UUS) offers an effective solution to urban problems but may also negatively affect urban development. Therefore, UUS development needs better concerted guidelines to coordinate various urban systems and the multiple components of the underground world. Sustainable Development Goals (SDGs), which should be viewed as important yardsticks for UUS development, do not explicitly mention urban underground space, although many of them are affected by both the positive and negative consequences of its development. To fill this gap, this review lays the foundations of relevant UUS concepts and uses exemplary cases to reveal that 11 out of 17 SDGs can be linked with UUS uses. These linkages also manifest that land administration, integrated planning, architectural design, and construction technology are critical dimensions for increasing the contributions of UUS to the realization of SDGs. To achieve multi-disciplinary synergies among these four critical dimensions, a collaborative approach framework based on spatial data infrastructure is required. Thus, this work provides academics and practitioners with a holistic view of sustainable UUS development.
We investigate the performance of a novel vertical slot fishway by employing finite volume and surrogate models. Multiple linear regression, multiple log equation regression, gene expression programming, and combinations of these models are employed to predict the maximum turbulence, maximum velocity, resting area, and water depth of the middle pool in the fishway. The statistical parameters and error terms, including the coefficient of determination, root mean square error, normalized square error, maximum positive and negative errors, and mean absolute percentage error were employed to evaluate and compare the accuracy of the models. We also conducted a parametric study. The independent variables include the opening between baffles (OBB), the ratio of the length of the large and small baffles, the volume flow rate, and the angle of the large baffle. The results show that the key parameters of the maximum turbulence and velocity are the volume flow rate and OBB.
In this study, a fully precast steel–ultrahigh performance concrete (UHPC) lightweight composite bridge (LWCB) was proposed based on Mapu Bridge, aiming at accelerating construction in bridge engineering. Cast-in-place joints are generally the controlling factor of segmental structures. Therefore, an innovative girder-to-girder joint that is suitable for LWCB was developed. A specimen consisting of two prefabricated steel–UHPC composite girder parts and one post-cast joint part was fabricated to determine if the joint can effectively transfer load between girders. The flexural behavior of the specimen under a negative bending moment was explored. Finite element analyses of Mapu Bridge showed that the nominal stress of critical sections could meet the required stress, indicating that the design is reasonable. The fatigue performance of the UHPC deck was assessed based on past research, and results revealed that the fatigue performance could meet the design requirements. Based on the test results, a crack width prediction method for the joint interface, a simplified calculation method for the design moment, and a deflection calculation method for the steel–UHPC composite girder in consideration of the UHPC tensile stiffness effect were presented. Good agreements were achieved between the predicted values and test results.
Slope failure occurs due to an increase in the saturation level and a subsequent decrease in matric suction in unsaturated soil. This paper presents the results of a series of centrifuge experiments and numerical analyses on a 55° inclined unsaturated sandy slope with less permeable, stronger silty sand layer inclusion within it. It is observed that a less permeable, stronger silty sand layer in an otherwise homogeneous sandy soil slope hinders the infiltration of water. The water content of the slope just above the stronger layer increases significantly, compared to elsewhere. No shear band is found to initiate in a homogeneous sandy soil slope, whereas for a non-homogeneous slope, they initiate just above the less pervious, stronger layer. A discontinuity of the shear zone is also observed for the case of a non-homogeneous soil slope. The factor of safety of a non-homogeneous, unsaturated soil slope decreases because of the less permeable, stronger layer. It decreases significantly if this less permeable, stronger soil layer is located near the toe of the slope.
The stability of the shield tunneling face is an extremely important factor affecting the safety of tunnel construction. In this study, a transparent clay with properties similar to those of Tianjin clay is prepared and a new transparent clay model test apparatus is developed to overcome the “black box” problem in the traditional model test. The stability of the shield tunneling face (failure mode, influence range, support force, and surface settlement) is investigated in transparent clay under active failure. A series of transparent clay model tests is performed to investigate the active failure mode, influence range, and support force of the shield tunneling face under different burial depth conditions, whereas particle flow code three-dimensional numerical simulations are conducted to verify the failure mode of the shield tunneling face and surface settlement along the transverse section under different burial depth conditions. The results show that the engineering characteristics of transparent clay are similar to those of soft clay in Binhai, Tianjin and satisfy visibility requirements. Two types of failure modes are obtained: the overall failure mode (cover/diameter: C/D≤1.0) and local failure mode (C/D≥2.0). The influence range of the transverse section is wider than that of the longitudinal section when C/D≥2.0. Additionally, the normalized thresholds of the relative displacement and support force ratio are 3%–6% and 0.2–0.4, respectively. Owing to the cushioning effect of the clay layer, the surface settlement is significantly reduced as the tunnel burial depth increases.
The analysis of cable structures is one of the most challenging problems for civil and mechanical engineers. Because they have highly nonlinear behavior, it is difficult to find solutions to these problems. Thus far, different assumptions and methods have been proposed to solve such structures. The dynamic relaxation method (DRM) is an explicit procedure for analyzing these types of structures. To utilize this scheme, investigators have suggested various stiffness matrices for a cable element. In this study, the efficiency and suitability of six well-known proposed matrices are assessed using the DRM. To achieve this goal, 16 numerical examples and two criteria, namely, the number of iterations and the analysis time, are employed. Based on a comprehensive comparison, the methods are ranked according to the two criteria. The numerical findings clearly reveal the best techniques. Moreover, a variety of benchmark problems are suggested by the authors for future studies of cable structures.
The wave of “digital age” featuring digital information is coming. Digital technology is profoundly changing the societal development direction and evolution paths. It also has significant bearing on production modes, social interactions and lifestyles. With regard to urban design, a system of knowledge about the creation and adaptation of material space forms that integrate humanities, art, technology and materials, digital technology has provided it with a brand-new and revolutionary scientific impetus for its evolution. The result of this evolution is “digital urban design paradigm based on human-computer interaction”, i.e., the urban development is moving toward “pan-dimensionality” and “individual ubiquity”. The future of urban design will construct a new approach to urban research and engineering, which is more complex, capable of accommodating and compatible with multiple goals of “instrumental rationality” and “value rationality”. Such a new approach shall be led by the probabilistic theory of “gray scale thinking”, reflecting quaternary synergetic view of “scientific rationality, ecological rationality, cultural rationality and technical rationality” to realize the cognitive progress of “engineering for the benefit of mankind”.
This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that were developed by ML algorithms-K2, hill climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning in Netica, thereby fixing the BBN models. The performance measure indexes, namely, overall accuracy (OA), precision, recall, F-measure, and area under the receiver operating characteristic curve, were used to evaluate the training and testing BBN models’ performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models. The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors, whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential. The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.
This paper reviews the fire problem in critical transportation infrastructures such as bridges and tunnels. The magnitude of the fire problem is illustrated, and the recent increase in fire problems in bridges and tunnels is highlighted. Recent research undertaken to address fire problems in transportation structures is reviewed, as well as critical factors governing the performance of those structures. Furthermore, key strategies recommended for mitigating fire hazards in bridges and tunnels are presented, and their applicability to practical situations is demonstrated through a practical case study. Furthermore, research needs and emerging trends for enhancing the “state-of-the-art” in this area are discussed.
Self-consolidating concrete (SCC) with manufactured sand (MSCC) is crucial to guarantee the quality of concrete construction technology and the associated property. The properties of MSCC with different microlimestone powder (MLS) replacements of retreated manufactured sand (TMsand) are investigated in this study. The result indicates that high-performance SCC, made using TMsand (TMSCC), achieved high workability, good mechanical properties, and durability by optimizing MLS content and adding fly ash and silica fume. In particular, the TMSCC with 12% MLS content exhibits the best workability, and the TMSCC with 4% MLS content has the highest strength in the late age, which is even better than that of SCC made with the river sand (Rsand). Though MLS content slightly affects the hydration reaction of cement and mainly plays a role in the nucleation process in concrete structures compared to silica fume and fly ash, increasing MLS content can evidently have a significant impact on the early age hydration progress. TMsand with MLS content ranging from 8% to 12% may be a suitable alternative for the Rsand used in the SCC as fine aggregate. The obtained results can be used to promote the application of SCC made with manufactured sand and mineral admixtures for concrete-based infrastructure.
Once a column in building is removed due to gas explosion, vehicle impact, terrorist attack, earthquake or any natural disaster, the loading supported by removed column transfers to neighboring structural elements. If these elements are unable to resist the supplementary loading, they continue to fail, which leads to progressive collapse of building. In this paper, an efficient strategy to model and simulate the progressive collapse of multi-story reinforced concrete structure under sudden column removal is presented. The strategy is subdivided into several connected steps including failure mechanism creation, MBS dynamic analysis and dynamic contact simulation, the latter is solved by using conserving/decaying scheme to handle the stiff nonlinear dynamic equations. The effect of gravity loads, structure-ground contact, and structure-structure contact are accounted for as well. The main novelty in this study consists in the introduction of failure function, and the proper manner to control the mechanism creation of a frame until its total failure. Moreover, this contribution pertains to a very thorough investigation of progressive collapse of the structure under sudden column removal. The proposed methodology is applied to a six-story frame, and many different progressive collapse scenarios are investigated. The results illustrate the efficiency of the proposed strategy.
Reinforced concrete beams consisting of both steel and glass-fiber-reinforced polymer rebars exhibit excellent strength, serviceability, and durability. However, the fatigue shear performance of such beams is unclear. Therefore, beams with hybrid longitudinal bars and hybrid stirrups were designed, and fatigue shear tests were performed. For specimens that failed by fatigue shear, all the glass-fiber-reinforced polymer stirrups and some steel stirrups fractured at the critical diagonal crack. For the specimen that failed by the static test after 8 million fatigue cycles, the static capacity after fatigue did not significantly decrease compared with the calculated value. The initial fatigue level has a greater influence on the crack development and fatigue life than the fatigue level in the later phase. The fatigue strength of the glass-fiber-reinforced polymer stirrups in the specimens was considerably lower than that of the axial tension tests on the glass-fiber-reinforced polymer bar in air and beam-hinge tests on the glass-fiber-reinforced polymer bar, and the failure modes were different. Glass-fiber-reinforced polymer stirrups were subjected to fatigue tension and shear, and failed owing to shear.
The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme learning machines, including the standard, online sequential, and kernel extreme learning machines, in addition to the artificial neural network, classification and regression tree model, and statistical multiple linear regression model. The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models. The input variability was assessed based on two scenarios prior to the application of the predictive models. For the first assessment, the machine learning models were developed using all the available cement and concrete mixture input variables; the second assessment was conducted based on the gamma test approach, which is a sensitivity analysis technique. Subsequently, the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches. The adopted methodology attained optimistic and reliable modeling results. The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.
In this study, the influences of spatially varying stochastic properties on free vibration analysis of composite plates were investigated via development of a new approach named the deterministic-stochastic Galerkin-based semi-analytical method. The material properties including tensile modulus, shear modulus, and density of the plate were assumed to be spatially varying and uncertain. Gaussian fields with first-order Markov kernels were utilized to define the aforementioned material properties. The stochastic fields were decomposed via application of the Karhunen-Loeve theorem. A first-order shear deformation theory was assumed, following which the displacement field was defined using admissible trigonometric modes to derive the potential and kinetic energies. The stochastic equations of motion of the plate were obtained using the variational principle. The deterministic-stochastic Galerkin-based method was utilized to find the probability space of natural frequencies, and the corresponding mode shapes of the plate were determined using a polynomial chaos approach. The proposed method significantly reduced the size of the mathematical models of the structure, which is very useful for enhancing the computational efficiency of stochastic simulations. The methodology was verified using a stochastic finite element method and the available results in literature. The sensitivity of natural frequencies and corresponding mode shapes due to the uncertainty of material properties was investigated, and the results indicated that the higher-order modes are more sensitive to uncertainty propagation in spatially varying properties.
This study presents the results of the 3D microstructure, thermal conductivity, and heat flow in cement-based foams and examines their changes with a range of densities. Images were captured using X-ray micro computed tomography (micro-CT) imaging technique on cement-based foam samples prepared with densities of 400, 600, and 800 kg/m3. These images were later simulated and quantified using 3D data visualization and analysis software. Based on the analysis, the pore volume of 11000 µm3 was determined across the three densities, leading to optimal results. However, distinct pore diameters of 15 µm for 800 kg/m3, and 20 µm for 600 and 400 kg/m3 were found to be optimum. Most of the pores were spherical, with only 10% appearing elongated or fractured. In addition, a difference of 15% was observed between the 2D and 3D porosity results. Moreover, a difference of 5% was noticed between the experimentally measured thermal conductivity and the numerically predicted value and this variation was constant across the three cast densities. The 3D model showed that heat flows through the cement paste solids and with an increase in porosity this flow reduces.